Update app.py

app.py

@@ -32,7 +32,13 @@ from analytics_plot_generator import (
     generate_engagement_rate_over_time_plot,
     generate_reach_over_time_plot,
     generate_impressions_over_time_plot,
-    create_placeholder_plot # For initializing plots
+    create_placeholder_plot, # For initializing plots
+    # --- Import new plot functions ---
+    generate_likes_over_time_plot,
+    generate_clicks_over_time_plot,
+    generate_shares_over_time_plot,
+    generate_comments_over_time_plot,
+    generate_comments_sentiment_breakdown_plot
 )
 
 # Configure logging
@@ -45,14 +51,18 @@ def update_analytics_plots(token_state_value, date_filter_option, custom_start_d
     """
     logging.info(f"Updating analytics plots. Filter: {date_filter_option}, Custom Start: {custom_start_date}, Custom End: {custom_end_date}")
 
+    # --- Increased number of expected plots ---
+    num_expected_plots = 18 # Was 13, added 5 new plots
+
     if not token_state_value or not token_state_value.get("token"):
         message = "❌ Access denied. No token. Cannot generate analytics."
         logging.warning(message)
-        num_expected_plots = 13 
         placeholder_figs = [create_placeholder_plot(title="Access Denied", message="No token.") for _ in range(num_expected_plots)]
         return [message] + placeholder_figs
 
     try:
+        # prepare_filtered_analytics_data might need to be updated if new DFs are required for new plots (e.g. comment sentiment)
+        # For now, we assume it returns the same set of DFs and new plots will try to use them or handle missing data.
         (filtered_merged_posts_df, 
          filtered_mentions_df, 
          date_filtered_follower_stats_df, 
@@ -61,18 +71,22 @@ def update_analytics_plots(token_state_value, date_filter_option, custom_start_d
             prepare_filtered_analytics_data(
                 token_state_value, date_filter_option, custom_start_date, custom_end_date
             )
+        
+        # Hypothetical: If prepare_filtered_analytics_data was updated to return comment sentiment data:
+        # filtered_comments_with_sentiment_df = ... # (This would be the 7th item in the tuple)
+        # For now, we will pass filtered_merged_posts_df to generate_comments_sentiment_breakdown_plot,
+        # and that function will handle missing sentiment columns by showing a placeholder.
+        # Or, if you have comment sentiment data in another DataFrame in token_state, retrieve it here.
+        # e.g., comments_df_with_sentiment = token_state_value.get("bubble_comments_sentiment_df", pd.DataFrame())
+
     except Exception as e:
         error_msg = f"❌ Error preparing analytics data: {e}"
         logging.error(error_msg, exc_info=True)
-        num_expected_plots = 13
         placeholder_figs = [create_placeholder_plot(title="Data Preparation Error", message=str(e)) for _ in range(num_expected_plots)]
         return [error_msg] + placeholder_figs
 
     date_column_posts = token_state_value.get("config_date_col_posts", "published_at")
     date_column_mentions = token_state_value.get("config_date_col_mentions", "date")
-    # This 'date_column_followers' from token_state is for the *source* DataFrame's date column,
-    # but the plot generator now uses 'date_info_column' for the 'category_name' that holds date strings.
-    # We'll use the default 'category_name' in the plot functions directly.
     # config_date_col_followers_source = token_state_value.get("config_date_col_followers", "date") 
 
 
@@ -80,22 +94,19 @@ def update_analytics_plots(token_state_value, date_filter_option, custom_start_d
     logging.info(f"Date-Filtered Follower Stats: {len(date_filtered_follower_stats_df)} rows, Raw Follower Stats: {len(raw_follower_stats_df)} rows.")
 
     try:
+        # Existing plots
         plot_posts_activity = generate_posts_activity_plot(filtered_merged_posts_df, date_column=date_column_posts)
         plot_engagement_type = generate_engagement_type_plot(filtered_merged_posts_df) 
         plot_mentions_activity = generate_mentions_activity_plot(filtered_mentions_df, date_column=date_column_mentions)
         plot_mention_sentiment = generate_mention_sentiment_plot(filtered_mentions_df) 
 
-        # Corrected calls for follower plots: use date_info_column (defaults to 'category_name' in plot generator)
         plot_followers_count = generate_followers_count_over_time_plot(
             date_filtered_follower_stats_df, 
-            # date_info_column is defaulted in the function to 'category_name'
-            # organic_count_col, paid_count_col are defaulted
-            type_filter_column='follower_count_type', # Ensure this column exists
+            type_filter_column='follower_count_type',
             type_value='follower_gains_monthly' 
         )
         plot_followers_growth_rate = generate_followers_growth_rate_plot(
             date_filtered_follower_stats_df,
-            # date_info_column is defaulted
             type_filter_column='follower_count_type',
             type_value='follower_gains_monthly'
         )
@@ -109,6 +120,24 @@ def update_analytics_plots(token_state_value, date_filter_option, custom_start_d
         plot_reach_over_time = generate_reach_over_time_plot(filtered_merged_posts_df, date_column=date_column_posts, reach_col='clickCount')
         plot_impressions_over_time = generate_impressions_over_time_plot(filtered_merged_posts_df, date_column=date_column_posts, impressions_col='impressionCount')
 
+        # --- Generate new plots ---
+        plot_likes_over_time = generate_likes_over_time_plot(filtered_merged_posts_df, date_column=date_column_posts, likes_col='likeCount')
+        plot_clicks_over_time = generate_clicks_over_time_plot(filtered_merged_posts_df, date_column=date_column_posts, clicks_col='clickCount')
+        plot_shares_over_time = generate_shares_over_time_plot(filtered_merged_posts_df, date_column=date_column_posts, shares_col='shareCount')
+        plot_comments_over_time = generate_comments_over_time_plot(filtered_merged_posts_df, date_column=date_column_posts, comments_col='commentCount')
+        
+        # For comment sentiment, pass a DataFrame that is expected to have comment-level sentiment.
+        # If `filtered_merged_posts_df` is passed and lacks 'comment_sentiment' column, the plot function will show a placeholder.
+        # If you have a specific df for this, e.g., `filtered_comments_with_sentiment_df` from `prepare_filtered_analytics_data` (if modified)
+        # or from `token_state_value.get("bubble_comments_sentiment_df")`, use that one.
+        # For this example, we assume `filtered_merged_posts_df` is passed and the plot function handles it.
+        plot_comments_sentiment_breakdown = generate_comments_sentiment_breakdown_plot(
+            filtered_merged_posts_df, # Or your specific df with comment sentiments
+            sentiment_column='sentiment' # Assuming 'sentiment' column in post_df might be a proxy, or change to 'comment_sentiment' if that column exists
+                                         # The plot function will show a placeholder if this column isn't suitable or found.
+        )
+
+
         message = f"📊 Analytics updated for period: {date_filter_option}"
         if date_filter_option == "Custom Range":
             s_display = start_dt_for_msg.strftime('%Y-%m-%d') if start_dt_for_msg else "Any"
@@ -119,23 +148,46 @@ def update_analytics_plots(token_state_value, date_filter_option, custom_start_d
             plot_posts_activity, plot_engagement_type, plot_mentions_activity, plot_mention_sentiment,
             plot_followers_count, plot_followers_growth_rate,
             plot_followers_by_location, plot_followers_by_role, plot_followers_by_industry, plot_followers_by_seniority,
-            plot_engagement_rate, plot_reach_over_time, plot_impressions_over_time
+            plot_engagement_rate, plot_reach_over_time, plot_impressions_over_time,
+            # --- Add new plot objects to the list ---
+            plot_likes_over_time, plot_clicks_over_time,
+            plot_shares_over_time, plot_comments_over_time,
+            plot_comments_sentiment_breakdown
         ]
         num_plots_generated = sum(1 for p in all_generated_plots if p is not None and not isinstance(p, str)) 
-        logging.info(f"Successfully generated {num_plots_generated} plots.")
+        logging.info(f"Successfully generated {num_plots_generated} plots out of {num_expected_plots} expected.")
+        
+        # Ensure the number of returned plots matches num_expected_plots, padding with placeholders if necessary
+        # This is crucial if some plot functions might return None on error and we need to match the Gradio outputs list length
+        final_plots_list = []
+        for p in all_generated_plots:
+            if p is not None and not isinstance(p, str): # isinstance check for safety, though plots should be figs
+                final_plots_list.append(p)
+            else: # If a plot failed and returned None or an error string (which it shouldn't, should be placeholder fig)
+                logging.warning(f"A plot generation failed or returned unexpected type, using placeholder. Plot: {p}")
+                final_plots_list.append(create_placeholder_plot(title="Plot Error", message="Failed to generate this plot."))
+
+        # If fewer plots were generated than expected (e.g. due to early exit or major error in a plot function)
+        while len(final_plots_list) < num_expected_plots:
+            logging.warning(f"Padding missing plot with placeholder. Expected {num_expected_plots}, got {len(final_plots_list)} so far.")
+            final_plots_list.append(create_placeholder_plot(title="Missing Plot", message="Plot could not be generated."))
+            if len(final_plots_list) > num_expected_plots + 5: # Safety break
+                 logging.error("Too many placeholders added, breaking loop.")
+                 break
+
+
+        return [message] + final_plots_list[:num_expected_plots] # Ensure correct number of outputs
 
-        return [message] + all_generated_plots
     except Exception as e:
         error_msg = f"❌ Error generating analytics plots: {e}"
         logging.error(error_msg, exc_info=True)
-        num_expected_plots = 13
         placeholder_figs = [create_placeholder_plot(title="Plot Generation Error", message=str(e)) for _ in range(num_expected_plots)]
         return [error_msg] + placeholder_figs
 
 
 # --- Gradio UI Blocks ---
 with gr.Blocks(theme=gr.themes.Soft(primary_hue="blue", secondary_hue="sky"),
-               title="LinkedIn Organization Dashboard") as app:
+                title="LinkedIn Organization Dashboard") as app:
 
     token_state = gr.State(value={
         "token": None, "client_id": None, "org_urn": None,
@@ -143,11 +195,12 @@ with gr.Blocks(theme=gr.themes.Soft(primary_hue="blue", secondary_hue="sky"),
         "bubble_post_stats_df": pd.DataFrame(), 
         "bubble_mentions_df": pd.DataFrame(),
         "bubble_follower_stats_df": pd.DataFrame(),
+        # Consider adding "bubble_comments_sentiment_df": pd.DataFrame() if you plan to fetch this data
         "fetch_count_for_api": 0, 
         "url_user_token_temp_storage": None,
         "config_date_col_posts": "published_at", 
-        "config_date_col_mentions": "date",     
-        "config_date_col_followers": "date" # This is for the original follower data's date column, if different from category_name for time series
+        "config_date_col_mentions": "date",       
+        "config_date_col_followers": "date" 
     })
 
     gr.Markdown("# 🚀 LinkedIn Organization Dashboard")
@@ -207,8 +260,8 @@ with gr.Blocks(theme=gr.themes.Soft(primary_hue="blue", secondary_hue="sky"),
                     label="Select Date Range (for Posts, Mentions, and some Follower time-series)",
                     value="Last 30 Days"
                 )
-                custom_start_date_picker = gr.DateTime(label="Start Date (Custom)", visible=False, include_time=False, type="string")
-                custom_end_date_picker = gr.DateTime(label="End Date (Custom)", visible=False, include_time=False, type="string")
+                custom_start_date_picker = gr.DateTime(label="Start Date (Custom)", visible=False, include_time=False, type="datetime") # Changed to datetime
+                custom_end_date_picker = gr.DateTime(label="End Date (Custom)", visible=False, include_time=False, type="datetime") # Changed to datetime
             
             apply_filter_btn = gr.Button("🔍 Apply Filter & Refresh Analytics", variant="primary")
 
@@ -248,9 +301,22 @@ with gr.Blocks(theme=gr.themes.Soft(primary_hue="blue", secondary_hue="sky"),
             gr.Markdown("### Post Performance Insights (Filtered by Date)")
             with gr.Row(): 
                 engagement_rate_plot = gr.Plot(label="Engagement Rate Over Time")
-            with gr.Row():
-                reach_over_time_plot = gr.Plot(label="Reach Over Time (Clicks)")
+                reach_over_time_plot = gr.Plot(label="Reach Over Time (Clicks)") # This was originally in its own row
+            with gr.Row(): # Moved impressions to be paired with reach if desired, or keep separate
                 impressions_over_time_plot = gr.Plot(label="Impressions Over Time")
+                # New plots will start here, keeping 2 per row
+                likes_over_time_plot = gr.Plot(label="Reactions (Likes) Over Time")
+
+            gr.Markdown("### Detailed Post Engagement Over Time (Filtered by Date)")
+            with gr.Row():
+                clicks_over_time_plot = gr.Plot(label="Clicks Over Time")
+                shares_over_time_plot = gr.Plot(label="Shares Over Time")
+            with gr.Row():
+                comments_over_time_plot = gr.Plot(label="Comments Over Time")
+                # For the 5th new plot, "Breakdown of Comments by Sentiment"
+                # It will be alone in this row, or you can add another plot next to it later.
+                comments_sentiment_plot = gr.Plot(label="Breakdown of Comments by Sentiment")
+
 
             analytics_plot_outputs = [
                 analytics_status_md, posts_activity_plot, engagement_type_plot, 
@@ -258,7 +324,11 @@ with gr.Blocks(theme=gr.themes.Soft(primary_hue="blue", secondary_hue="sky"),
                 followers_count_plot, followers_growth_rate_plot,
                 followers_by_location_plot, followers_by_role_plot, 
                 followers_by_industry_plot, followers_by_seniority_plot,
-                engagement_rate_plot, reach_over_time_plot, impressions_over_time_plot
+                engagement_rate_plot, reach_over_time_plot, impressions_over_time_plot,
+                # --- Add new plot components to the output list in the correct order ---
+                likes_over_time_plot, clicks_over_time_plot,
+                shares_over_time_plot, comments_over_time_plot,
+                comments_sentiment_plot
             ]
 
             apply_filter_btn.click(
@@ -268,6 +338,7 @@ with gr.Blocks(theme=gr.themes.Soft(primary_hue="blue", secondary_hue="sky"),
                 show_progress="full"
             )
             
+            # Also update analytics after sync
             sync_click_event.then( 
                 fn=update_analytics_plots,
                 inputs=[token_state, date_filter_selector, custom_start_date_picker, custom_end_date_picker],